Flow embedding in the data plane of multimedia IP communications
Bibliographic record
Abstract
The quality of multimedia communications heavily relies on the end-to-end network condition. Media sessions that are routed through highly-utilized links are in more jeopardy of longer delays, more packet loss. Choosing optimal routes to embed the data flows can be performed in a centralized manner in SDN-enabled networks. However, the calculation of such optimal flow embedding is NP-hard. In this paper, we propose a Two-phase Flow Embedding heuristic to tackle the problem. Phase I is a global planning module, periodically invoked. Given the total traffic of a time period, it produces the optimal end-to-end tunnels between each pair of communication endpoints. The objective is to balance the traffic distribution, measured by the value of network criticality. Phase II is a traffic engineering module, always active. It receives the individual data flows, and selects one of the pre-configured tunnels between the requested endpoints, to embed the flow. We compare the Two-phase flow embedding heuristic with Dijkstra algorithm. We show that the proposed heuristic outperforms the Dijkstra's in three different metrics: session accept rate, maximum/average substrate link utilization, and network criticality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".